ALIBI: Adversarial Legitimacy Injection in Binary Input against LLM Malware Analyzers
ALIBI embeds fake benign-narrative sections into binaries, flipping 30 of 35 malware samples to benign verdicts on Gemini 2.5 Pro.
ALIBI is a semantic cover-story attack against LLM-based malware analyzers that adds a small non-executed read-only section containing a false security-product narrative, without altering imports or executable behavior. On a frozen PE set, Gemini 2.5 Pro flipped 30 of 35 baseline-malicious samples to benign, while GPT-5.5 Pro and Claude Opus 4.7 showed substantial severity downgrades and confidence reductions. The attack transfers to ELF binaries (Gemini flips 16 of 40), and a verification-guided defense prompt only halves benign verdicts.
- Attacks LLM malware triage via false benign narrative, not instructions
- Gemini 2.5 Pro: 30/35 PE samples flipped to benign
- Transfers to ELF: 16/40 flipped on Gemini
- Defense prompt halves but does not eliminate misclassification
- Products
- ALIBI
From the original article
1 min read at arxiv.orgLarge language models are being integrated into malware triage workflows as reasoning components that summarize static evidence and produce analyst-facing verdicts. This paper shows that the same reasoning capability introduces a new attack surface. We present ALIBI, a semantic cover story attack against frontier LLM-based malware analyzers.
By Hyeongjun Choi · arxiv.org
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